# Google Releases HEIR: Compiler for Private AI on Encrypted Data

> Homomorphic encryption has long been the cryptographer's dream that never quite worked in practice: it lets you compute on data while it stays encrypted, so a server can process your information without ever seeing it. The catch has always been speed and the sheer difficulty of writing code for it. Google's new open-source project, HEIR, aims squarely at that second problem. It is a compiler that takes an ordinary pretrained model and rewrites it to run inference directly on encrypted inputs, with no decryption required. Built on the MLIR compiler framework, HEIR is designed to target all the mainstream encryption schemes and to plug into specialized hardware from partners like Belfort, Niobium, and Cornami, with academic collaborators at CMU, Georgia Tech, and UC Santa Barbara. The pitch is concrete: private recommendations, fraud detection, and other services that run on your data without exposing it. Why it matters is simple. Making this practical would let sensitive AI features run on data people would never hand over in the clear. It is early and still slow, but turning a research curiosity into real developer tooling is how these things eventually ship.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Google · Published Saturday, August 15, 2026_

## Wortins' read

Homomorphic encryption has long been the cryptographer's dream that never quite worked in practice: it lets you compute on data while it stays encrypted, so a server can process your information without ever seeing it. The catch has always been speed and the sheer difficulty of writing code for it. Google's new open-source project, HEIR, aims squarely at that second problem. It is a compiler that takes an ordinary pretrained model and rewrites it to run inference directly on encrypted inputs, with no decryption required. Built on the MLIR compiler framework, HEIR is designed to target all the mainstream encryption schemes and to plug into specialized hardware from partners like Belfort, Niobium, and Cornami, with academic collaborators at CMU, Georgia Tech, and UC Santa Barbara. The pitch is concrete: private recommendations, fraud detection, and other services that run on your data without exposing it. Why it matters is simple. Making this practical would let sensitive AI features run on data people would never hand over in the clear. It is early and still slow, but turning a research curiosity into real developer tooling is how these things eventually ship.

## Source

[Read the full story at Google](https://blog.google/security/how-google-is-making-private-ai-practical-with-homomorphic-encryption/)

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